| Description | Title: Infusing Structure into Machine Learning Algorithms Standard deep learning algorithms are based on a function-fitting approach that do not exploit any domain knowledge or constraints. This has several shortcomings: high sample complexity, and lack of robustness and generalization, especially under domain or task shifts. I will show several ways to infuse structure and domain knowledge to overcome these limitations, viz., tensors, graphs, symbolic rules, physical laws, and simulations. |
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